If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
"Scientia potentia est" is a Latin adage that means "knowledge is power". This phrase is commonly attributed to Sir Francis Bacon and its most common modern interpretation is'information is power'. There has never been a time in human history when this phrase was more relevant, as each day humanity creates over 2 Quintillion bytes of data. This reality has manufactured the big data boom that the world is currently experiencing. All of this data has to be processed, analyzed and stored in some way.
USA TODAY's Ed Baig looks at the top Tech trends to watch for in 2018. Visitors walk past a 5G logo during the Mobile World Congress in Barcelona, on March 1, 2017. Blistering fast wireless networks, digital assistants that are, well, everywhere, and a coming out bash for augmented reality. These and other technologies mentioned here, some of which are already familiar but really just getting started, are worth keeping an eye on in 2018. You can bet we'll also learn about innovations in the months to come that are for now, completely under the radar.
The Programmer's Apprentice project uses the domain of programming as a vehicle for studying (and attempting to duplicate) human problem solving behavior. Recognizing that it will be a long time before it is possible to fully duplicate an expert programmer's abilities, the project seeks to develop an intelligent assistant system, the Programmer's Apprentice (PA), which will help a programmer in various phases of the programming task. The Knowledge-Based Editor in Emacs (KBEmacs) is an initial step in the direction of the PA. A question that has been asked about KBEmacs is, "Where's the AI?" Going beyond this, the article uses the development of KBEmacs as an example that illustrates a number of general features of the process of developing an applied AI system. As part of this, the article compares the way AI ideas are used in KBEmacs with the way they were used in the initial proposal for the PA.
The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. The KBSE field is concerned with applying knowledge-based AI techniques to the problems of creating, understanding, and maintaining very large software systems. The Sixth Annual Knowledge-Based Software Engineering Conference (KBSE-91) was held at the Sheraton University Inn and Conference Center in Syracuse, New York, from Sunday afternoon, 22 September, through midday Wednesday, 25 September. This conference was sponsored by Rome Laboratory (previously Rome Air Development Center) and was held in cooperation with the Association for Computing Machinery and the American Association for Artificial Intelligence. The origin of KBSE-91 is as follows: In 1983, Rome Air Development Center published a report calling for the development of a knowledgebased software assistant (KBSA) that would use AI techniques to support all phases of the software development process (Green et al. 1986).
Artificial Intelligence Department, Computer Resenrch Laboratory, Tektronix, 1, Post Office Box 500, Beaverton, Oregon 97077 Getting started on a new knowledge engineering project is a difficult and challenging task, even for those who have done it before. For those who haven't, the task can often prove impossible. One reason is that the requirementsoriented methods and intuitions learned in the development of other types of software do not carry over well to the knowledge engineering task. Another reason is that methodologies for developing expert systems by extracting, representing, and manipulating an expert's knowledge have been slow in coming. At Tektronix, we have been using a step-by-step approach to prototyping expert systems for over two years now.
Mixed-initiative planning systems attempt to integrate human and AI planners so that the synthesis results in high-quality plans. In the AI community, the dominant model of planning is search. In state-space planning, search consists of backward and forward chaining through the effects and preconditions of operator representations. Although search is an acceptable mechanism to use in performing automated planning, we present an alternative model to present to the user at the interface of a mixed-initiative planning assistant. That is, we propose to model planning as a goal-manipulation task.
The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report. The Cognitive and Metacognitive Educational Systems (MCES) AAAI symposium, held in November 2010, was the second edition of this successful AAAI symposium. The idea for the symposium stemmed from several theoretical, conceptual, empirical, and applied considerations about the role of metacognition and self-regulation when learning with computer-based learning environments (CBLEs). A related goal was the design and implementation issues associated with metacognitive educational systems. MCES implemented as CBLEs are designed to interact with users and support their learning and decision-making processes. A critical component of good decision making is self-regulation. The primary aim of this symposium was to continue the discussion started in 2009 on some of the previous considerations and to enhance the discussions with some new ones: What are the theoretical foundations and how are they articulated in CBLEs? Is it possible to develop a unified framework for all metacognitive educational systems? What are the necessary characteristics of these systems to support metacognition? To what extent does the educational system itself have to exhibit metacognitive behaviors, and how are these behaviors organized and enacted to support learning? What are the main aspects of metacognition, self-regulation skills, emotions, and motivations that influence the learning process? What does it mean to be metacognitive, and how can one learn to be metacognitive? Can MCES actually foster learners to be self-regulating agents? How can an MCES be autonomous and increase its knowledge to match the learners' evolving skills and knowledge?
This case study article describes the iterative design process of an AIbased mixed-initiative calendaring tool with embedded artificial intelligence. We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users. These findings motivated the redesign and enhancement of our intelligent system. Lessons learned from the study--namely, that AI systems must be usable to gain widespread adoption and retention and that simple problems that perhaps do not necessitate complex AIbased solutions should not go unattended merely because of their inherent simplicity--conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system. We are working within the infrastructure of a general-purpose, computerized office assistant named CALO (Myers et al. 2007).
The field of knowledge engineering has been one of the most visible successes of AI to date. Knowledge acquisition is the main bottleneck in the knowledge engineer's work. Machine-learning tools have contributed positively to the process of trying to eliminate or open up this bottleneck, but how do we know whether the field is progressing? How can we determine the progress made in any of its branches? How can we be sure of an advance and take advantage of it?
We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire- Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences. While doing so, she must maintain awareness of deadlines and resources, as well as tracking current activities and new information that could affect her objectives and productivity.